Tarefa causcibench_real.q127

Veredito

Pergunta feita ao analista

What is the effect of term limits on state expenditure per capita?

Como o agente resolveu e como foi validado

Resumo passo a passo do que o agente executou, por que o caminho está correto e como cada verificação conferiu o resultado.

1. 1Preparação da base de dados
   keele_do_term_limits— This dataset contains state-level panel data examining fiscal policy outcomes across U.S. states during a period of significant variation in legislative term limits. The data spans multiple decades, capturing the adoption and subsequent repeal of term limits across states. The term limits movement began in 1990 with California, Colorado, and Oklahoma, followed by a major wave in 1992 when eight additional states adopted restrictions. By 2000, twenty-one states had implemented term limits on state legislators. However, several states later repealed these limits through court decisions or legislative action. Currently, fifteen states maintain active term limits while six have repealed previously enacted restrictions. Variable description: exp_real_pc: real per capita expenditure tl_onset: whether a term limit was active in that year population: population popgrowth: relative population growth with respect to the previous year populationdensity: population density grants_real_pc: federal grants per capita gsp_real_inc: real gross state product per capita fedempciv_pc: federal employment per capita fedempmil_pc: federal military employment per capita govtempsandl_pc: state and local employment per capita seats_house: number of seats in the house of representatives seats_senate: number of seats in the senate unemployment: unemployment rate gopshare: share of votes for the republican party demcontrol: whether the state is controlled by the democratic party dividedgov: whether the state has a divided government squire_score: squire score initiatives: number of initiatives tel: whether the state has implemented tax and expenditure limitations debtlimit: whether the state has a debt limit year: year fips: state code (Federal Information Processing Standards code) state: state name, may contain spelling errors
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_90de0dc8 nos detalhes

   causcibench_metadata_rule/1: did

   Por que este é o caminho de referência: causcibench_metadata_rule/1: did
   Argumentos principais

   controls:

   ["population","popgrowth","populationdensity","grants_real_pc","gsp_real_inc","fedempciv_pc","fedempmil_pc","govtempsandl_pc","seats_house","seats_senate","unemployment","gopshare","demcontrol","dividedgov","squire_score","initiatives","tel","debtlimit"]

   dataset:

   "keele_do_term_limits"

   fe:

   ["fips","year"]

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["tl_onset"]

   y:

   "exp_real_pc"

   Resultado calculado

   • coef.tl_onset = -5,55924 -5.559238248588588
     Coefficient on tl_onset in the regression of exp_real_pc
   Diagnósticos: n_obs = 1.3631363 (observations used) · k = 9393 (parameters estimated, fixed-effect dummies included) · r2 = 0,9235780.9235775439610645 · df_resid = 1.2701270 · se_type = "HC1" (heteroskedasticity-robust standard errors (HC1)) · fixed_effects = "fips (47 levels), year (29 levels)" (absorbed as dummies; their coefficients are not tabulated)
3. 3Conferência de lastro e resposta final

   Todos os 1 números escritos na resposta foram conferidos contra as ferramentas e o enunciado (nenhum número sem lastro).

   Números encontrados: -5,559
   Figuras citadas: call_90de0dc8:coef.tl_onset
   • ✓
     #0resposta com lastro

     every number in the answer was stated by a tool call
   • ✓
     #1figura da resposta próxima

     CauSciBench effect accuracy: |answer figure − gold| ≤ 5% of |gold|

Valores esperados e de onde vêm

Verificações

Caminho de referência e caminho do agente

A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

1. 0Início

   What is the effect of term limits on state expenditure per capita?

   keele_do_term_limits (agent)

   This dataset contains state-level panel data examining fiscal policy outcomes across U.S. states during a period of significant variation in legislative term limits. The data spans multiple decades, capturing the adoption and subsequent repeal of term limits across states. The term limits movement began in 1990 with California, Colorado, and Oklahoma, followed by a major wave in 1992 when eight additional states adopted restrictions. By 2000, twenty-one states had implemented term limits on state legislators. However, several states later repealed these limits through court decisions or legislative action. Currently, fifteen states maintain active term limits while six have repealed previously enacted restrictions. Variable description: exp_real_pc: real per capita expenditure tl_onset: whether a term limit was active in that year population: population popgrowth: relative population growth with respect to the previous year populationdensity: population density grants_real_pc: federal grants per capita gsp_real_inc: real gross state product per capita fedempciv_pc: federal employment per capita fedempmil_pc: federal military employment per capita govtempsandl_pc: state and local employment per capita seats_house: number of seats in the house of representatives seats_senate: number of seats in the senate unemployment: unemployment rate gopshare: share of votes for the republican party demcontrol: whether the state is controlled by the democratic party dividedgov: whether the state has a divided government squire_score: squire score initiatives: number of initiatives tel: whether the state has implemented tax and expenditure limitations debtlimit: whether the state has a debt limit year: year fips: state code (Federal Information Processing Standards code) state: state name, may contain spelling errors

   file 57dfc507d57b56b5790e4f71220a32a302ae01cc57be41652fbd60ce19e8ba99 · frame —
2. 1Turno do modelo
   call_f6a33eaereferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: did

   Chamadas pedidas

   • infer.ols

     {
       "controls": [
         "population",
         "popgrowth",
         "populationdensity",
         "grants_real_pc",
         "gsp_real_inc",
         "fedempciv_pc",
         "fedempmil_pc",
         "govtempsandl_pc",
         "seats_house",
         "seats_senate",
         "unemployment",
         "gopshare",
         "demcontrol",
         "dividedgov",
         "squire_score",
         "initiatives",
         "tel",
         "debtlimit"
       ],
       "dataset": "keele_do_term_limits",
       "fe": [
         "fips",
         "year"
       ],
       "x": [
         "tl_onset"
       ],
       "y": "exp_real_pc"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_90de0dc8decidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: did

   Argumentos

   {
     "cluster": null,
     "controls": [
       "population",
       "popgrowth",
       "populationdensity",
       "grants_real_pc",
       "gsp_real_inc",
       "fedempciv_pc",
       "fedempmil_pc",
       "govtempsandl_pc",
       "seats_house",
       "seats_senate",
       "unemployment",
       "gopshare",
       "demcontrol",
       "dividedgov",
       "squire_score",
       "initiatives",
       "tel",
       "debtlimit"
     ],
     "dataset": "keele_do_term_limits",
     "fe": [
       "fips",
       "year"
     ],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "tl_onset"
     ],
     "y": "exp_real_pc"
   }

   Figuras call_90de0dc8

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.tl_onset Coefficient on tl_onset in the regression of exp_real_pc	-5,55924	-5.559238248588588	3,218723.2187239380607915	-11,8738 – 0,755363

   Diagnósticos

   • n_obs = 1.3631363 (observations used)
   • k = 9393 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,9235780.9235775439610645
   • df_resid = 1.2701270
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)
   • fixed_effects = "fips (47 levels), year (29 levels)" (absorbed as dummies; their coefficients are not tabulated)

   Derivação matemática e pressupostos

   coef.tl_onset

   1. Coeficiente de tl_onset na regressão por mínimos quadrados ordinários de exp_real_pc, efeitos fixos de fips, year.→ -5,55924 (-5.559238248588588)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 3,21872 (3.2187239380607915)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 1270 graus de liberdade vezes o erro-padrão.IC = β ± c · ep

   Pressupostos do estimador

   • the conditional mean of the outcome is linear in the regressors
   • errors are independent across observations; their variance may differ

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": -5.27262357023173,
         "ci_low": -194.0497557398846,
         "coef": -99.66118965505817,
         "p": 0.0385207911901036,
         "se": 48.1124203994311,
         "t": -2.071423321206193,
         "term": "Intercept"
       },
       {
         "ci_high": 0.7553627341559048,
         "ci_low": -11.87383923133308,
         "coef": -5.559238248588588,
         "p": 0.08438279804490278,
         "se": 3.2187239380607915,
         "t": -1.7271559647758743,
         "term": "tl_onset"
       },
       {
         "ci_high": -0.0000021507801053412907,
         "ci_low": -0.000007628881489064911,
         "coef": -0.000004889830797203101,
         "p": 0.0004772313649846642,
         "se": 0.0000013961686658506498,
         "t": -3.502320970814692,
         "term": "population"
       },
       {
         "ci_high": 0.5578816310836541,
         "ci_low": -1.7164197889673354,
         "coef": -0.5792690789418407,
         "p": 0.31780786867328076,
         "se": 0.5796366582059279,
         "t": -0.9993658453810962,
         "term": "popgrowth"
       },
       {
         "ci_high": 0.37765881379895927,
         "ci_low": 0.08835813260077852,
         "coef": 0.2330084731998689,
         "p": 0.001613847830030332,
         "se": 0.07373221446726812,
         "t": 3.160199037603952,
         "term": "populationdensity"
       },
       {
         "ci_high": 0.0000011169272230152201,
         "ci_low": 6.374398296926654e-7,
         "coef": 8.771835263539428e-7,
         "p": 1.1998084226118369e-12,
         "se": 1.2220388549514508e-7,
         "t": 7.17803302898083,
         "term": "grants_real_pc"
       },
       {
         "ci_high": 0.0523975672548958,
         "ci_low": 0.031625034616737664,
         "coef": 0.04201130093581673,
         "p": 4.575781773150611e-15,
         "se": 0.0052941625479817015,
         "t": 7.9354006521452085,
         "term": "gsp_real_inc"
       },
       {
         "ci_high": 0.01974292124078359,
         "ci_low": -0.014824739402870352,
         "coef": 0.0024590909189566206,
         "p": 0.7801953110188954,
         "se": 0.008810038599472415,
         "t": 0.27912373949234254,
         "term": "fedempciv_pc"
       },
       {
         "ci_high": 0.010095810026358815,
         "ci_low": -0.02137544553115226,
         "coef": -0.005639817752396722,
         "p": 0.4820959934220905,
         "se": 0.008020877637446787,
         "t": -0.7031422254924206,
         "term": "fedempmil_pc"
       },
       {
         "ci_high": 0.03028725753410803,
         "ci_low": 0.012307054192219485,
         "coef": 0.021297155863163757,
         "p": 0.000003709865727539808,
         "se": 0.004582499437880874,
         "t": 4.647497757907503,
         "term": "govtempsandl_pc"
       },
       {
         "ci_high": 0.6104954912358547,
         "ci_low": 0.019012741068008043,
         "coef": 0.3147541161519314,
         "p": 0.03700056835986174,
         "se": 0.15074742585618042,
         "t": 2.087956821579298,
         "term": "seats_house"
       },
       {
         "ci_high": 2.622183968612376,
         "ci_low": -1.2055128338438252,
         "coef": 0.7083355673842754,
         "p": 0.46791406418955356,
         "se": 0.9755406049702443,
         "t": 0.7260954221437875,
         "term": "seats_senate"
       },
       {
         "ci_high": 4.1132028332950625,
         "ci_low": -0.032831852464846545,
         "coef": 2.0401854904151078,
         "p": 0.05373477947914511,
         "se": 1.0566733454380284,
         "t": 1.9307627084786347,
         "term": "unemployment"
       },
       {
         "ci_high": 41.841646508966605,
         "ci_low": -73.00832286565888,
         "coef": -15.58333817834614,
         "p": 0.5945559103557495,
         "se": 29.271077200430433,
         "t": -0.5323800716878635,
         "term": "gopshare"
       },
       {
         "ci_high": 7.6569656975384826,
         "ci_low": -4.5641523920388964,
         "coef": 1.5464066527497933,
         "p": 0.6196391428441786,
         "se": 3.1147182104049462,
         "t": 0.49648364580266285,
         "term": "demcontrol"
       },
       {
         "ci_high": 11.832696644383585,
         "ci_low": 2.261922848690163,
         "coef": 7.047309746536874,
         "p": 0.003928564513405139,
         "se": 2.439241909832789,
         "t": 2.8891393338760607,
         "term": "dividedgov"
       },
       {
         "ci_high": 88.49859916125413,
         "ci_low": 1.4770686627976275,
         "coef": 44.98783391202588,
         "p": 0.04272447145708398,
         "se": 22.17862095383983,
         "t": 2.028432426238704,
         "term": "squire_score"
       },
       {
         "ci_high": 18.888905716456527,
         "ci_low": -33.020068961245975,
         "coef": -7.065581622394723,
         "p": 0.5933870300831844,
         "se": 13.229708405319922,
         "t": -0.5340693389397393,
         "term": "initiatives"
       },
       {
         "ci_high": 4.04862330097067,
         "ci_low": -9.45887584310616,
         "coef": -2.705126271067745,
         "p": 0.4321389319643361,
         "se": 3.4425699230388687,
         "t": -0.7857868776939299,
         "term": "tel"
       },
       {
         "ci_high": -19.255719369288045,
         "ci_low": -78.87770022156202,
         "coef": -49.066709795425034,
         "p": 0.0012738685938721635,
         "se": 15.195472962442725,
         "t": -3.229034720847372,
         "term": "debtlimit"
       }
     ]
   }

   • dropped as collinear with the other regressors: ['fips[50]']

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   keele_do_term_limits 05bca1170ff33ca31ffea371b35a9489153b050608e2719369fc9accaa9127c8

   sha256 do resultado

   f7730a0c3e2c8a4df3f2c8bc4217cc25bbacd58678a614588f263ea335a098e0

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_15afe552referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.ols): coef.tl_onset = -5,559.",
       "caveats": [],
       "figure_refs": [
         "call_90de0dc8:coef.tl_onset"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   -5,559

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa (infer.ols): coef.tl_onset = -5,559.

   Figuras citadas: call_90de0dc8:coef.tl_onset · Parada: answered

   Figura da resposta: call_90de0dc8:coef.tl_onset = -5,55924-5.559238248588588 (se 3,218723.2187239380607915)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória-5.559238249 vs -5.559

Épocas × verificações

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

• época 1, tentativa 1passoupontuada(exibindo)

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